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Li X, Jing K, Yang Y, Wang Y, Ma J, Zheng H, Xu Z. Noise-Generating and Imaging Mechanism Inspired Implicit Regularization Learning Network for Low Dose CT Reconstrution. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1677-1689. [PMID: 38145543 DOI: 10.1109/tmi.2023.3347258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
Abstract
Low-dose computed tomography (LDCT) helps to reduce radiation risks in CT scanning while maintaining image quality, which involves a consistent pursuit of lower incident rays and higher reconstruction performance. Although deep learning approaches have achieved encouraging success in LDCT reconstruction, most of them treat the task as a general inverse problem in either the image domain or the dual (sinogram and image) domains. Such frameworks have not considered the original noise generation of the projection data and suffer from limited performance improvement for the LDCT task. In this paper, we propose a novel reconstruction model based on noise-generating and imaging mechanism in full-domain, which fully considers the statistical properties of intrinsic noises in LDCT and prior information in sinogram and image domains. To solve the model, we propose an optimization algorithm based on the proximal gradient technique. Specifically, we derive the approximate solutions of the integer programming problem on the projection data theoretically. Instead of hand-crafting the sinogram and image regularizers, we propose to unroll the optimization algorithm to be a deep network. The network implicitly learns the proximal operators of sinogram and image regularizers with two deep neural networks, providing a more interpretable and effective reconstruction procedure. Numerical results demonstrate our proposed method improvements of > 2.9 dB in peak signal to noise ratio, > 1.4% promotion in structural similarity metric, and > 9 HU decrements in root mean square error over current state-of-the-art LDCT methods.
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Wang L, Meng M, Chen S, Bian Z, Zeng D, Meng D, Ma J. Semi-supervised iterative adaptive network for low-dose CT sinogram recovery. Phys Med Biol 2024; 69:085013. [PMID: 38422540 DOI: 10.1088/1361-6560/ad2ee7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 02/29/2024] [Indexed: 03/02/2024]
Abstract
Background.Concern has been expressed regarding the risk of carcinogenesis from medical computed tomography (CT) radiation. Lowering radiation in CT without appropriate modifications often leads to severe noise-induced artifacts in the images. The utilization of deep learning (DL) techniques has achieved promising reconstruction performance in low-dose CT (LDCT) imaging. However, most DL-based algorithms require the pre-collection of a large set of image pairs (low-dose/standard-dose) and the training of networks in an end-to-end supervised manner. Meanwhile, securing such a large volume of paired, well-registered training data in clinical practice is challenging. Moreover, these algorithms often overlook the potential to utilize the abundant information in a large collection of LDCT-only images/sinograms.Methods.In this paper, we introduce a semi-supervised iterative adaptive network (SIA-Net) for LDCT imaging, utilizing both labeled and unlabeled sinograms in a cohesive network framework, integrating supervised and unsupervised learning processes. Specifically, the supervised process captures critical features (i.e. noise distribution and tissue characteristics) latent in the paired sinograms, while the unsupervised process effectively learns these features in the unlabeled low-dose sinograms, employing a conventional weighted least-squares model with a regularization term. Furthermore, the SIA-Net method is designed to adaptively transfer the learned feature distribution from the supervised to the unsupervised process, thereby obtaining a high-fidelity sinogram through iterative adaptive learning. Finally, high-quality CT images can be reconstructed from the refined sinogram using the filtered back-projection algorithm.Results.Experimental results on two clinical datasets indicate that the proposed SIA-Net method achieves competitive performance in terms of noise reduction and structure preservation in LDCT imaging, when compared to traditional supervised learning methods.
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Affiliation(s)
- Lei Wang
- School of Future Technology, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Mingqiang Meng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
- Pazhou Lab (Huangpu), Guangdong, People's Republic of China
| | - Shixuan Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
- Pazhou Lab (Huangpu), Guangdong, People's Republic of China
| | - Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
- Pazhou Lab (Huangpu), Guangdong, People's Republic of China
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangdong, People's Republic of China
| | - Deyu Meng
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
- School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China
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An R, Chen K, Li H. Self-supervised dual-domain balanced dropblock-network for low-dose CT denoising. Phys Med Biol 2024; 69:075026. [PMID: 38359449 DOI: 10.1088/1361-6560/ad29ba] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 02/15/2024] [Indexed: 02/17/2024]
Abstract
Objective.Self-supervised learning methods have been successfully applied for low-dose computed tomography (LDCT) denoising, with the advantage of not requiring labeled data. Conventional self-supervised methods operate only in the image domain, ignoring valuable priors in the sinogram domain. Recently proposed dual-domain methods address this limitation but encounter issues with blurring artifacts in the reconstructed image due to the inhomogeneous distribution of noise levels in low-dose sinograms.Approach.To tackle this challenge, this paper proposes SDBDNet, an end-to-end dual-domain self-supervised method for LDCT denoising. With the network designed based on the properties of inhomogeneous noise in low-dose sinograms and the principle of moderate sinogram-domain denoising, SDBDNet achieves effective denoising in dual domains without introducing blurring artifacts. Specifically, we split the sinogram into two subsets based on the positions of detector cells to generate paired training data with high similarity and independent noise. These sub-sinograms are then restored to their original size using 1D interpolation and learning-based correction. To achieve adaptive and moderate smoothing in the sinogram domain, we integrate Dropblock, a type of convolution layer with regularization, into SDBDNet, and set a weighted average between the denoised sinograms and their noisy counterparts, leading to a well-balanced dual-domain approach.Main results.Numerical experiments show that our method outperforms popular non-learning and self-supervised learning methods, demonstrating its effectiveness and superior performance.Significance.While introducing a novel high-performance dual-domain self-supervised LDCT denoising method, this paper also emphasizes and verifies the importance of appropriate sinogram-domain denoising in dual-domain methods, which might inspire future work.
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Affiliation(s)
- Ran An
- School of Mathematical Sciences, Capital Normal University, Beijing, 100048, People's Republic of China
- Centre for Mathematical Imaging Techniques, University of Liverpool, Liverpool, L69 7ZL, United Kingdom
| | - Ke Chen
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, G1 1XQ, United Kingdom
| | - Hongwei Li
- School of Mathematical Sciences, Capital Normal University, Beijing, 100048, People's Republic of China
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Gibson NM, Lee A, Bencsik M. A practical method to simulate realistic reduced-exposure CT images by the addition of computationally generated noise. Radiol Phys Technol 2024; 17:112-123. [PMID: 37955819 DOI: 10.1007/s12194-023-00755-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 11/14/2023]
Abstract
Computed tomography (CT) scanning protocols should be optimized to minimize the radiation dose necessary for imaging. The addition of computationally generated noise to the CT images facilitates dose reduction. The objective of this study was to develop a noise addition method that reproduces the complexity of the noise texture present in clinical images with directionality that varies over images according to the underlying anatomy, requiring only Digital Imaging and Communications in Medicine (DICOM) images as input data and commonly available phantoms for calibration. The developed method is based on the estimation of projection data by forward projection from images, the addition of Poisson noise, and the reconstruction of new images. The method was validated by applying it to images acquired from cylindrical and thoracic phantoms using source images with exposures up to 49 mAs and target images between 39 and 5 mAs. 2D noise spectra were derived for regions of interest in the generated low-dose images and compared with those from the scanner-acquired low-dose images. The root mean square difference between the standard deviations of noise was 4%, except for very low exposures in peripheral regions of the cylindrical phantom. The noise spectra from the corresponding regions of interest exhibited remarkable agreement, indicating that the complex nature of the noise was reproduced. A practical method for adding noise to CT images was presented, and the magnitudes of noise and spectral content were validated. This method may be used to optimize CT imaging.
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Affiliation(s)
- Nicholas Mark Gibson
- Medical Physics and Clinical Engineering, Queens Medical Centre, Nottingham University Hospitals NHS Trust, Derby Road, Nottingham, NG7 2UH, UK.
| | - Amy Lee
- Physics and Mathematics, Nottingham Trent University, Clifton Lane, Clifton, Nottingham, NG11 8NS, UK
| | - Martin Bencsik
- Physics and Mathematics, Nottingham Trent University, Clifton Lane, Clifton, Nottingham, NG11 8NS, UK
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陈 世, 曾 栋, 边 兆, 马 建. [A low- dose CT reconstruction algorithm across different scanners based on federated feature learning]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2024; 44:333-343. [PMID: 38501419 PMCID: PMC10954523 DOI: 10.12122/j.issn.1673-4254.2024.02.16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Indexed: 03/20/2024]
Abstract
OBJECTIVE To propose a low-dose CT reconstruction algorithm across different scanners based on federated feature learning (FedCT) to improve the generalization of deep learning models for multiple CT scanners and protect data privacy. METHODS In the proposed FedCT framework, each client is assigned an inverse Radon transform-based reconstruction model to serve as a local network model that participates in federated learning. A projection- domain specific learning strategy is adopted to preserve the geometry specificity in the local projection domain. Federated feature learning is introduced in the model, which utilizes conditional parameters to mark the local data and feed the conditional parameters into the network for encoding to enhance the generalization of the model in the image domain. RESULTS In the cross-client, multi-scanner, and multi-protocol low-dose CT reconstruction experiments, FedCT achieved the highest PSNR (+2.8048, +2.7301, and +2.7263 compared to the second best federated learning method), the highest SSIM (+0.0009, +0.0165, and +0.0131 in the same comparison), and the lowest RMSE (- 0.6687, - 1.5956, and - 0.9962). In the ablation experiment, compared with the general federated learning strategy, the model with projection-specific learning strategy showed an average improvement by 1.18 on Q1 of the PSNR and an average decrease by 1.36 on Q3 of the RMSE on the test set. The introduction of federated feature learning in FedCT further improved the Q1 of the PSNR on the test set by 3.56 and reduced the Q3 of the RMSE by 1.80. CONCLUSION FedCT provides an effective solution for collaborative construction of CT reconstruction models, which can enhance model generalization and further improve the reconstruction performance on global data while protecting data privacy.
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Affiliation(s)
- 世宣 陈
- />南方医科大学生物医学工程学院//广州市医用放射成像与检测技术重点实验室,广东 广州 510515School of Biomedical Engineering, Southern Medical University//Guangzhou Key Laboratory of Medical Radioimaging and Detection Technology, Guangzhou 510515, China
| | - 栋 曾
- />南方医科大学生物医学工程学院//广州市医用放射成像与检测技术重点实验室,广东 广州 510515School of Biomedical Engineering, Southern Medical University//Guangzhou Key Laboratory of Medical Radioimaging and Detection Technology, Guangzhou 510515, China
| | - 兆英 边
- />南方医科大学生物医学工程学院//广州市医用放射成像与检测技术重点实验室,广东 广州 510515School of Biomedical Engineering, Southern Medical University//Guangzhou Key Laboratory of Medical Radioimaging and Detection Technology, Guangzhou 510515, China
| | - 建华 马
- />南方医科大学生物医学工程学院//广州市医用放射成像与检测技术重点实验室,广东 广州 510515School of Biomedical Engineering, Southern Medical University//Guangzhou Key Laboratory of Medical Radioimaging and Detection Technology, Guangzhou 510515, China
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Chi J, Wei X, Sun Z, Yang Y, Yang B. Low-Dose CT Image Super-resolution Network with Noise Inhibition Based on Feedback Feature Distillation Mechanism. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-00979-1. [PMID: 38378965 DOI: 10.1007/s10278-024-00979-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 02/22/2024]
Abstract
Low-dose computed tomography (LDCT) has been widely used in medical diagnosis. In practice, doctors often zoom in on LDCT slices for clearer lesions and issues, while, a simple zooming operation fails to suppress low-dose artifacts, leading to distorted details. Therefore, numerous LDCT super-resolution (SR) methods have been proposed to promote the quality of zooming without the increase of the dose in CT scanning. However, there are still some drawbacks that need to be addressed in existing methods. First, the region of interest (ROI) is not emphasized due to the lack of guidance in the reconstruction process. Second, the convolutional blocks extracting fix-resolution features fail to concentrate on the essential multi-scale features. Third, a single SR head cannot suppress the residual artifacts. To address these issues, we propose an LDCT CT joint SR and denoising reconstruction network. Our proposed network consists of global dual-guidance attention fusion modules (GDAFMs) and multi-scale anastomosis blocks (MABs). The GDAFM directs the network to focus on ROI by fusing the extra mask guidance and average CT image guidance, while the MAB introduces hierarchical features through anastomosis connections to leverage multi-scale features and promote the feature representation ability. To suppress radial residual artifacts, we optimize our network using the feedback feature distillation mechanism (FFDM) which shares the backbone to learn features corresponding to the denoising task. We apply the proposed method to the 3D-IRCADB and PANCREAS datasets to evaluate its ability on LDCT image SR reconstruction. The experimental results compared with state-of-the-art methods illustrate the superiority of our approach with respect to peak signal-to-noise (PSNR), structural similarity (SSIM), and qualitative observations. Our proposed LDCT joint SR and denoising reconstruction network has been extensively evaluated through ablation, quantitative, and qualitative experiments. The results demonstrate that our method can recover noise-free and detail-sharp images, resulting in better reconstruction results. Code is available at https://github.com/neu-szy/ldct_sr_dn_w_ffdm .
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Affiliation(s)
- Jianning Chi
- Faculty of Robot Science and Engineering, Northeastern University, Zhihui Street, Shenyang, 110169, Liaoning, China
- Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Zhihui Street, Shenyang, 110169, Liaoning, China
| | - Xiaolin Wei
- Department of Rehabilitation, the Second Hospital of Beijing, No. 36 Youfang Hutong, 100031, Beijing, China
| | - Zhiyi Sun
- Faculty of Robot Science and Engineering, Northeastern University, Zhihui Street, Shenyang, 110169, Liaoning, China.
| | - Yongming Yang
- Faculty of Robot Science and Engineering, Northeastern University, Zhihui Street, Shenyang, 110169, Liaoning, China
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
| | - Bin Yang
- Department of Radiology, the Second Hospital of Beijing, No. 36 Youfang Hutong, 100031, Beijing, China
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Choi MH, Lee SW, Pak S. Low-dose versus conventional CT urography using dual-source CT with different time-current product values and the same tube voltage: image quality and diagnostic performance in various diagnoses. Br J Radiol 2024; 97:399-407. [PMID: 38308025 DOI: 10.1093/bjr/tqad029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 10/05/2023] [Accepted: 11/14/2023] [Indexed: 02/04/2024] Open
Abstract
OBJECTIVES To compare the image quality and diagnostic performance of low-dose CT urography to that of concurrently acquired conventional CT using dual-source CT. METHODS This retrospective study included 357 consecutive CT urograms performed by third-generation dual-source CT in a single institution between April 2020 and August 2021. Two-phase CT images (unenhanced phase, excretory phase with split bolus) were obtained with two different tube current-time products (280 mAs for the conventional-dose protocol and 70 mAs for the low-dose protocol) and the same tube voltage (90 kVp) for the two X-ray tubes. Iterative reconstruction was applied for both protocols. Two radiologists independently performed quantitative and qualitative image quality analysis and made diagnoses. The correlation between the noise level or the effective radiation dose and the patients' body weight was evaluated. RESULTS Significantly higher noise levels resulting in a significantly lower liver signal-to-noise ratio and contrast-to-noise ratio were noted in low-dose images compared to conventional images (P < .001). Qualitative analysis by both radiologists showed significantly lower image quality in low-dose CT than in conventional CT images (P < .001). Patient's body weight was positively correlated with noise and effective radiation dose (P < .001). Diagnostic performance for various diseases, including urolithiasis, inflammation, and mass, was not different between the two protocols. CONCLUSIONS Despite inferior image quality, low-dose CT urography with 70 mAs and 90 kVp and iterative reconstruction demonstrated diagnostic performance equivalent to that of conventional CT for identifying various diseases of the urinary tract. ADVANCES IN KNOWLEDGE Low-dose CT (25% radiation dose) with low tube current demonstrated diagnostic performance comparable to that of conventional CT for a variety of urinary tract diseases.
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Affiliation(s)
- Moon Hyung Choi
- Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 03312, Republic of Korea
| | - Sheen-Woo Lee
- Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 03312, Republic of Korea
| | - Seongyong Pak
- Siemens Healthineers Ltd, Seoul 06620, Republic of Korea
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Chao L, Wang Y, Zhang T, Shan W, Zhang H, Wang Z, Li Q. Joint denoising and interpolating network for low-dose cone-beam CT reconstruction under hybrid dose-reduction strategy. Comput Biol Med 2024; 168:107830. [PMID: 38086140 DOI: 10.1016/j.compbiomed.2023.107830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 11/12/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024]
Abstract
Cone-beam computed tomography (CBCT) is generally reconstructed with hundreds of two-dimensional X-Ray projections through the FDK algorithm, and its excessive ionizing radiation of X-Ray may impair patients' health. Two common dose-reduction strategies are to either lower the intensity of X-Ray, i.e., low-intensity CBCT, or reduce the number of projections, i.e., sparse-view CBCT. Existing efforts improve the low-dose CBCT images only under a single dose-reduction strategy. In this paper, we argue that applying the two strategies simultaneously can reduce dose in a gentle manner and avoid the extreme degradation of the projection data in a single dose-reduction strategy, especially under ultra-low-dose situations. Therefore, we develop a Joint Denoising and Interpolating Network (JDINet) in projection domain to improve the CBCT quality with the hybrid low-intensity and sparse-view projections. Specifically, JDINet mainly includes two important components, i.e., denoising module and interpolating module, to respectively suppress the noise caused by the low-intensity strategy and interpolate the missing projections caused by the sparse-view strategy. Because FDK actually utilizes the projection information after ramp-filtering, we develop a filtered structural similarity constraint to help JDINet focus on the reconstruction-required information. Afterward, we employ a Postprocessing Network (PostNet) in the reconstruction domain to refine the CBCT images that are reconstructed with denoised and interpolated projections. In general, a complete CBCT reconstruction framework is built with JDINet, FDK, and PostNet. Experiments demonstrate that our framework decreases RMSE by approximately 8 %, 15 %, and 17 %, respectively, on the 1/8, 1/16, and 1/32 dose data, compared to the latest methods. In conclusion, our learning-based framework can be deeply imbedded into the CBCT systems to promote the development of CBCT. Source code is available at https://github.com/LianyingChao/FusionLowDoseCBCT.
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Affiliation(s)
- Lianying Chao
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China; MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yanli Wang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China; MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - TaoTao Zhang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China; MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei, China; Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, China
| | - Wenqi Shan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China; MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Haobo Zhang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China; MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zhiwei Wang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China; MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qiang Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China; MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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Ikuta M, Zhang J. A Deep Convolutional Gated Recurrent Unit for CT Image Reconstruction. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:10612-10625. [PMID: 35522637 DOI: 10.1109/tnnls.2022.3169569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Computed tomography (CT) is one of the most important medical imaging technologies in use today. Most commercial CT products use a technique known as the filtered backprojection (FBP) that is fast and can produce decent image quality when an X-ray dose is high. However, the FBP is not good enough on low-dose X-ray CT imaging because the CT image reconstruction problem becomes more stochastic. A more effective reconstruction technique proposed recently and implemented in a limited number of CT commercial products is an iterative reconstruction (IR). The IR technique is based on a Bayesian formulation of the CT image reconstruction problem with an explicit model of the CT scanning, including its stochastic nature, and a prior model that incorporates our knowledge about what a good CT image should look like. However, constructing such prior knowledge is more complicated than it seems. In this article, we propose a novel neural network for CT image reconstruction. The network is based on the IR formulation and constructed with a recurrent neural network (RNN). Specifically, we transform the gated recurrent unit (GRU) into a neural network performing CT image reconstruction. We call it "GRU reconstruction." This neural network conducts concurrent dual-domain learning. Many deep learning (DL)-based methods in medical imaging are single-domain learning, but dual-domain learning performs better because it learns from both the sinogram and the image domain. In addition, we propose backpropagation through stage (BPTS) as a new RNN backpropagation algorithm. It is similar to the backpropagation through time (BPTT) of an RNN; however, it is tailored for iterative optimization. Results from extensive experiments indicate that our proposed method outperforms conventional model-based methods, single-domain DL methods, and state-of-the-art DL techniques in terms of the root mean squared error (RMSE), the peak signal-to-noise ratio (PSNR), and the structure similarity (SSIM) and in terms of visual appearance.
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Huang Z, Li W, Wang Y, Liu Z, Zhang Q, Jin Y, Wu R, Quan G, Liang D, Hu Z, Zhang N. MLNAN: Multi-level noise-aware network for low-dose CT imaging implemented with constrained cycle Wasserstein generative adversarial networks. Artif Intell Med 2023; 143:102609. [PMID: 37673577 DOI: 10.1016/j.artmed.2023.102609] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 05/17/2023] [Accepted: 06/06/2023] [Indexed: 09/08/2023]
Abstract
Low-dose CT techniques attempt to minimize the radiation exposure of patients by estimating the high-resolution normal-dose CT images to reduce the risk of radiation-induced cancer. In recent years, many deep learning methods have been proposed to solve this problem by building a mapping function between low-dose CT images and their high-dose counterparts. However, most of these methods ignore the effect of different radiation doses on the final CT images, which results in large differences in the intensity of the noise observable in CT images. What'more, the noise intensity of low-dose CT images exists significantly differences under different medical devices manufacturers. In this paper, we propose a multi-level noise-aware network (MLNAN) implemented with constrained cycle Wasserstein generative adversarial networks to recovery the low-dose CT images under uncertain noise levels. Particularly, the noise-level classification is predicted and reused as a prior pattern in generator networks. Moreover, the discriminator network introduces noise-level determination. Under two dose-reduction strategies, experiments to evaluate the performance of proposed method are conducted on two datasets, including the simulated clinical AAPM challenge datasets and commercial CT datasets from United Imaging Healthcare (UIH). The experimental results illustrate the effectiveness of our proposed method in terms of noise suppression and structural detail preservation compared with several other deep-learning based methods. Ablation studies validate the effectiveness of the individual components regarding the afforded performance improvement. Further research for practical clinical applications and other medical modalities is required in future works.
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Affiliation(s)
- Zhenxing Huang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Wenbo Li
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Yunling Wang
- Department of Radiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, China.
| | - Zhou Liu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - Qiyang Zhang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yuxi Jin
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Ruodai Wu
- Department of Radiology, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen 518055, China
| | - Guotao Quan
- Shanghai United Imaging Healthcare, Shanghai 201807, China
| | - Dong Liang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Zhanli Hu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Na Zhang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
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11
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Niu C, Li M, Fan F, Wu W, Guo X, Lyu Q, Wang G. Noise Suppression With Similarity-Based Self-Supervised Deep Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:1590-1602. [PMID: 37015446 PMCID: PMC10288330 DOI: 10.1109/tmi.2022.3231428] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Image denoising is a prerequisite for downstream tasks in many fields. Low-dose and photon-counting computed tomography (CT) denoising can optimize diagnostic performance at minimized radiation dose. Supervised deep denoising methods are popular but require paired clean or noisy samples that are often unavailable in practice. Limited by the independent noise assumption, current self-supervised denoising methods cannot process correlated noises as in CT images. Here we propose the first-of-its-kind similarity-based self-supervised deep denoising approach, referred to as Noise2Sim, that works in a nonlocal and nonlinear fashion to suppress not only independent but also correlated noises. Theoretically, Noise2Sim is asymptotically equivalent to supervised learning methods under mild conditions. Experimentally, Nosie2Sim recovers intrinsic features from noisy low-dose CT and photon-counting CT images as effectively as or even better than supervised learning methods on practical datasets visually, quantitatively and statistically. Noise2Sim is a general self-supervised denoising approach and has great potential in diverse applications.
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12
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Tivnan M, Gang GJ, Wang W, Noël P, Sulam J, Webster Stayman J. Tunable neural networks for CT image formation. J Med Imaging (Bellingham) 2023; 10:033501. [PMID: 37151806 PMCID: PMC10157542 DOI: 10.1117/1.jmi.10.3.033501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 04/03/2023] [Indexed: 05/09/2023] Open
Abstract
Optimization of CT image quality typically involves balancing variance and bias. In traditional filtered back-projection, this trade-off is controlled by the filter cutoff frequency. In model-based iterative reconstruction, the regularization strength parameter often serves the same function. Deep neural networks (DNNs) typically do not provide this tunable control over output image properties. Models are often trained to minimize the expected mean squared error, which penalizes both variance and bias in image outputs but does not offer any control over the trade-off between the two. We propose a method for controlling the output image properties of neural networks with a new loss function called weighted covariance and bias (WCB). Our proposed method uses multiple noise realizations of the input images during training to allow for separate weighting matrices for the variance and bias penalty terms. Moreover, we show that tuning these weights enables targeted penalization of specific image features with spatial frequency domain penalties. To evaluate our method, we present a simulation study using digital anthropomorphic phantoms, physical simulation of CT measurements, and image formation with various algorithms. We show that the WCB loss function offers a greater degree of control over trade-offs between variance and bias, whereas mean-squared error provides only one specific image quality configuration. We also show that WCB can be used to control specific image properties including variance, bias, spatial resolution, and the noise correlation of neural network outputs. Finally, we present a method to optimize the proposed weights for a spiculated lung nodule shape discrimination task. Our results demonstrate this new image quality can control the image properties of DNN outputs and optimize image quality for task-specific performance.
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Affiliation(s)
- Matthew Tivnan
- Johns Hopkins University, Department of Biomedical Engineering, Baltimore, Maryland, United States
| | - Grace J. Gang
- Johns Hopkins University, Department of Biomedical Engineering, Baltimore, Maryland, United States
| | - Wenying Wang
- Johns Hopkins University, Department of Biomedical Engineering, Baltimore, Maryland, United States
| | - Peter Noël
- Hospital of the University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Jeremias Sulam
- Johns Hopkins University, Department of Biomedical Engineering, Baltimore, Maryland, United States
| | - J. Webster Stayman
- Johns Hopkins University, Department of Biomedical Engineering, Baltimore, Maryland, United States
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13
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Ikuta M, Zhang J. TextureWGAN: texture preserving WGAN with multitask regularizer for computed tomography inverse problems. J Med Imaging (Bellingham) 2023; 10:024003. [PMID: 36895762 PMCID: PMC9990134 DOI: 10.1117/1.jmi.10.2.024003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 01/31/2023] [Indexed: 03/09/2023] Open
Abstract
Purpose This paper presents a deep learning (DL) based method called TextureWGAN. It is designed to preserve image texture while maintaining high pixel fidelity for computed tomography (CT) inverse problems. Over-smoothed images by postprocessing algorithms have been a well-known problem in the medical imaging industry. Therefore, our method tries to solve the over-smoothing problem without compromising pixel fidelity. Approach The TextureWGAN extends from Wasserstein GAN (WGAN). The WGAN can create an image that looks like a genuine image. This aspect of the WGAN helps preserve image texture. However, an output image from the WGAN is not correlated to the corresponding ground truth image. To solve this problem, we introduce the multitask regularizer (MTR) to the WGAN framework to make a generated image highly correlated to the corresponding ground truth image so that the TextureWGAN can achieve high-level pixel fidelity. The MTR is capable of using multiple objective functions. In this research, we adopt a mean squared error (MSE) loss to maintain pixel fidelity. We also use a perception loss to improve the look and feel of result images. Furthermore, the regularization parameters in the MTR are trained along with generator network weights to maximize the performance of the TextureWGAN generator. Results The proposed method was evaluated in CT image reconstruction applications in addition to super-resolution and image-denoising applications. We conducted extensive qualitative and quantitative evaluations. We used PSNR and SSIM for pixel fidelity analysis and the first-order and the second-order statistical texture analysis for image texture. The results show that the TextureWGAN is more effective in preserving image texture compared with other well-known methods such as the conventional CNN and nonlocal mean filter (NLM). In addition, we demonstrate that TextureWGAN can achieve competitive pixel fidelity performance compared with CNN and NLM. The CNN with MSE loss can attain high-level pixel fidelity, but it often damages image texture. Conclusions TextureWGAN can preserve image texture while maintaining pixel fidelity. The MTR is not only helpful to stabilize the TextureWGAN's generator training but also maximizes the generator performance.
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Affiliation(s)
- Masaki Ikuta
- University of Wisconsin - Milwaukee, Department of Electrical Engineering and Computer Science, Milwaukee, Wisconsin, United States
- GE Healthcare, Computed Tomography Engineering - Image Reconstruction, Waukesha, Wisconsin, United States
| | - Jun Zhang
- University of Wisconsin - Milwaukee, Department of Electrical Engineering and Computer Science, Milwaukee, Wisconsin, United States
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14
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Tivnan M, Lee TC, Zhang R, Boedeker K, Cai L, Sulam J, Stayman JW. Task-Driven CT Image Quality Optimization for Low-Contrast Lesion Detectability with Tunable Neural Networks. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2023; 12463:124631M. [PMID: 38188182 PMCID: PMC10769460 DOI: 10.1117/12.2653936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Low-contrast lesions are difficult to detect in noisy low-dose CT images. Improving CT image quality for this detection task has the potential to improve diagnostic accuracy and patient outcomes. In this work, we use tunable neural networks for CT image restoration with a hyperparameter to control the variance/bias tradeoff. We use clinical images from a super-high-resolution normal-dose CT scan to synthesize low-contrast low-dose CT images for supervised training of deep learning CT reconstruction models. Those models are trained using with multiple noise realizations so that variance and bias can be penalized separately. We use a training loss function with one hyperparameter called the denoising level, which controls the variance/bias tradeoff. Finally, we evaluate the CT image quality to find the optimal denoising level for low-contrast lesion detectability. We evaluate performance using a shallow neural network model classification model to represent a suboptimal image observer. Our results indicate that the optimal networks for low-contrast lesion detectability are those that prioritize bias reduction rather than mean-squared error, which demonstrates the potential clinical benefit of our proposed tunable neural networks.
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Affiliation(s)
- Matthew Tivnan
- Department of Biomedical Engineering, Johns Hopkins University. Baltimore, MD, USA
- Canon Medical Research, USA. Vernon Hills, IL, USA
| | | | | | | | - Liang Cai
- Canon Medical Research, USA. Vernon Hills, IL, USA
| | - Jeremias Sulam
- Department of Biomedical Engineering, Johns Hopkins University. Baltimore, MD, USA
| | - J Webster Stayman
- Department of Biomedical Engineering, Johns Hopkins University. Baltimore, MD, USA
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Alkabbany I, Ali AM, Mohamed M, Elshazly SM, Farag A. An AI-Based Colonic Polyp Classifier for Colorectal Cancer Screening Using Low-Dose Abdominal CT. SENSORS (BASEL, SWITZERLAND) 2022; 22:9761. [PMID: 36560132 PMCID: PMC9782078 DOI: 10.3390/s22249761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 11/30/2022] [Accepted: 12/02/2022] [Indexed: 06/17/2023]
Abstract
Among the non-invasive Colorectal cancer (CRC) screening approaches, Computed Tomography Colonography (CTC) and Virtual Colonoscopy (VC), are much more accurate. This work proposes an AI-based polyp detection framework for virtual colonoscopy (VC). Two main steps are addressed in this work: automatic segmentation to isolate the colon region from its background, and automatic polyp detection. Moreover, we evaluate the performance of the proposed framework on low-dose Computed Tomography (CT) scans. We build on our visualization approach, Fly-In (FI), which provides "filet"-like projections of the internal surface of the colon. The performance of the Fly-In approach confirms its ability with helping gastroenterologists, and it holds a great promise for combating CRC. In this work, these 2D projections of FI are fused with the 3D colon representation to generate new synthetic images. The synthetic images are used to train a RetinaNet model to detect polyps. The trained model has a 94% f1-score and 97% sensitivity. Furthermore, we study the effect of dose variation in CT scans on the performance of the the FI approach in polyp visualization. A simulation platform is developed for CTC visualization using FI, for regular CTC and low-dose CTC. This is accomplished using a novel AI restoration algorithm that enhances the Low-Dose CT images so that a 3D colon can be successfully reconstructed and visualized using the FI approach. Three senior board-certified radiologists evaluated the framework for the peak voltages of 30 KV, and the average relative sensitivities of the platform were 92%, whereas the 60 KV peak voltage produced average relative sensitivities of 99.5%.
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Affiliation(s)
- Islam Alkabbany
- Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY 40292, USA
| | - Asem M. Ali
- Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY 40292, USA
| | - Mostafa Mohamed
- Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY 40292, USA
| | | | - Aly Farag
- Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY 40292, USA
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Zhu M, Mao Z, Li D, Wang Y, Zeng D, Bian Z, Ma J. Structure-preserved meta-learning uniting network for improving low-dose CT quality. Phys Med Biol 2022; 67. [PMID: 36351294 DOI: 10.1088/1361-6560/aca194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 11/09/2022] [Indexed: 11/10/2022]
Abstract
Objective.Deep neural network (DNN) based methods have shown promising performances for low-dose computed tomography (LDCT) imaging. However, most of the DNN-based methods are trained on simulated labeled datasets, and the low-dose simulation algorithms are usually designed based on simple statistical models which deviate from the real clinical scenarios, which could lead to issues of overfitting, instability and poor robustness. To address these issues, in this work, we present a structure-preserved meta-learning uniting network (shorten as 'SMU-Net') to suppress noise-induced artifacts and preserve structure details in the unlabeled LDCT imaging task in real scenarios.Approach.Specifically, the presented SMU-Net contains two networks, i.e., teacher network and student network. The teacher network is trained on simulated labeled dataset and then helps the student network train with the unlabeled LDCT images via the meta-learning strategy. The student network is trained on real LDCT dataset with the pseudo-labels generated by the teacher network. Moreover, the student network adopts the Co-teaching strategy to improve the robustness of the presented SMU-Net.Main results.We validate the proposed SMU-Net method on three public datasets and one real low-dose dataset. The visual image results indicate that the proposed SMU-Net has superior performance on reducing noise-induced artifacts and preserving structure details. And the quantitative results exhibit that the presented SMU-Net method generally obtains the highest signal-to-noise ratio (PSNR), the highest structural similarity index measurement (SSIM), and the lowest root-mean-square error (RMSE) values or the lowest natural image quality evaluator (NIQE) scores.Significance.We propose a meta learning strategy to obtain high-quality CT images in the LDCT imaging task, which is designed to take advantage of unlabeled CT images to promote the reconstruction performance in the LDCT environments.
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Affiliation(s)
- Manman Zhu
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Zerui Mao
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Danyang Li
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Yongbo Wang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
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17
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Liu X, Liang X, Deng L, Tan S, Xie Y. Learning low-dose CT degradation from unpaired data with flow-based model. Med Phys 2022; 49:7516-7530. [PMID: 35880375 DOI: 10.1002/mp.15886] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 07/13/2022] [Accepted: 07/17/2022] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND There has been growing interest in low-dose computed tomography (LDCT) for reducing the X-ray radiation to patients. However, LDCT always suffers from complex noise in reconstructed images. Although deep learning-based methods have shown their strong performance in LDCT denoising, most of them require a large number of paired training data of normal-dose CT (NDCT) images and LDCT images, which are hard to acquire in the clinic. Lack of paired training data significantly undermines the practicability of supervised deep learning-based methods. To alleviate this problem, unsupervised or weakly supervised deep learning-based methods are required. PURPOSE We aimed to propose a method that achieves LDCT denoising without training pairs. Specifically, we first trained a neural network in a weakly supervised manner to simulate LDCT images from NDCT images. Then, simulated training pairs could be used for supervised deep denoising networks. METHODS We proposed a weakly supervised method to learn the degradation of LDCT from unpaired LDCT and NDCT images. Concretely, LDCT and normal-dose images were fed into one shared flow-based model and projected to the latent space. Then, the degradation between low-dose and normal-dose images was modeled in the latent space. Finally, the model was trained by minimizing the negative log-likelihood loss with no requirement of paired training data. After training, an NDCT image can be input to the trained flow-based model to generate the corresponding LDCT image. The simulated image pairs of NDCT and LDCT can be further used to train supervised denoising neural networks for test. RESULTS Our method achieved much better performance on LDCT image simulation compared with the most widely used image-to-image translation method, CycleGAN, according to the radial noise power spectrum. The simulated image pairs could be used for any supervised LDCT denoising neural networks. We validated the effectiveness of our generated image pairs on a classic convolutional neural network, REDCNN, and a novel transformer-based model, TransCT. Our method achieved mean peak signal-to-noise ratio (PSNR) of 24.43dB, mean structural similarity (SSIM) of 0.785 on an abdomen CT dataset, mean PSNR of 33.88dB, mean SSIM of 0.797 on a chest CT dataset, which outperformed several traditional CT denoising methods, the same network trained by CycleGAN-generated data, and a novel transfer learning method. Besides, our method was on par with the supervised networks in terms of visual effects. CONCLUSION We proposed a flow-based method to learn LDCT degradation from only unpaired training data. It achieved impressive performance on LDCT synthesis. Next, we could train neural networks with the generated paired data for LDCT denoising. The denoising results are better than traditional and weakly supervised methods, comparable to supervised deep learning methods.
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Affiliation(s)
- Xuan Liu
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaokun Liang
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Lei Deng
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Shan Tan
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
| | - Yaoqin Xie
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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18
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Chi J, Sun Z, Wang H, Lyu P, Yu X, Wu C. CT image super-resolution reconstruction based on global hybrid attention. Comput Biol Med 2022; 150:106112. [PMID: 36209555 DOI: 10.1016/j.compbiomed.2022.106112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/17/2022] [Accepted: 09/17/2022] [Indexed: 11/03/2022]
Abstract
Computer tomography (CT) has played an essential role in the field of medical diagnosis, but the blurry edges and unclear textures in traditional CT images usually interfere the subsequent judgement from radiologists or clinicians. Deep learning based image super-resolution methods have been applied for CT image restoration recently. However, different levels of information of CT image details are mixed and difficult to be mapped from deep features by traditional convolution operations. Moreover, features representing regions of interest (ROIs) in CT images are treated equally as those for background, resulting in low concentration of meaningful features and high redundancy of computation. To tackle these issues, a CT image super-resolution network is proposed based on hybrid attention mechanism and global feature fusion, which consists of the following three parts: 1) stacked Swin Transformer blocks are used as the backbone to extract initial features from the degraded CT image; 2) a multi-branch hierarchical self-attention module (MHSM) is proposed to adaptively map multi-level features representing different levels of image information from the initial features and establish the relationship between these features through a self-attention mechanism, where three branches apply different strategies of integrating convolution, down-sampling and up-sampling operations according to three different scale factors; 3) a multidimensional local topological feature enhancement module (MLTEM) is proposed and plugged into the end of the backbone to refine features in the channel and spatial dimension simultaneously, so that the features representing ROIs could be enhanced while meaningless ones eliminated. Experimental results demonstrate that our method outperform the state-of-the-art super-resolution methods on restoring CT images with respect to peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) indices.
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Affiliation(s)
- Jianning Chi
- Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110167, China; Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang 110167, China.
| | - Zhiyi Sun
- Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110167, China.
| | - Huan Wang
- Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110167, China.
| | - Pengfei Lyu
- Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110167, China.
| | - Xiaosheng Yu
- Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110167, China.
| | - Chengdong Wu
- Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110167, China.
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Zeng D, Zeng C, Zeng Z, Li S, Deng Z, Chen S, Bian Z, Ma J. Basis and current state of computed tomography perfusion imaging: a review. Phys Med Biol 2022; 67. [PMID: 35926503 DOI: 10.1088/1361-6560/ac8717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 08/04/2022] [Indexed: 12/30/2022]
Abstract
Computed tomography perfusion (CTP) is a functional imaging that allows for providing capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we aim to offer insight into CTP imaging which covers the basics and current state of CTP imaging, then summarize the technical applications in the CTP imaging as well as the future technological potential. At first, we focus on the fundamentals of CTP imaging including systematically summarized CTP image acquisition and hemodynamic parameter map estimation techniques. A short assessment is presented to outline the clinical applications with CTP imaging, and then a review of radiation dose effect of the CTP imaging on the different applications is presented. We present a categorized methodology review on known and potential solvable challenges of radiation dose reduction in CTP imaging. To evaluate the quality of CTP images, we list various standardized performance metrics. Moreover, we present a review on the determination of infarct and penumbra. Finally, we reveal the popularity and future trend of CTP imaging.
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Affiliation(s)
- Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Cuidie Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhixiong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Sui Li
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhen Deng
- Department of Neurology, Nanfang Hospital, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Sijin Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
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20
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Shibata H, Hanaoka S, Nomura Y, Nakao T, Takenaga T, Hayashi N, Abe O. On the Simulation of Ultra-Sparse-View and Ultra-Low-Dose Computed Tomography with Maximum a Posteriori Reconstruction Using a Progressive Flow-Based Deep Generative Model. Tomography 2022; 8:2129-2152. [PMID: 36136875 PMCID: PMC9498355 DOI: 10.3390/tomography8050179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 08/17/2022] [Accepted: 08/20/2022] [Indexed: 11/17/2022] Open
Abstract
Ultra-sparse-view computed tomography (CT) algorithms can reduce radiation exposure for patients, but these algorithms lack an explicit cycle consistency loss minimization and an explicit log-likelihood maximization in testing. Here, we propose X2CT-FLOW for the maximum a posteriori (MAP) reconstruction of a three-dimensional (3D) chest CT image from a single or a few two-dimensional (2D) projection images using a progressive flow-based deep generative model, especially for ultra-low-dose protocols. The MAP reconstruction can simultaneously optimize the cycle consistency loss and the log-likelihood. We applied X2CT-FLOW for the reconstruction of 3D chest CT images from biplanar projection images without noise contamination (assuming a standard-dose protocol) and with strong noise contamination (assuming an ultra-low-dose protocol). We simulated an ultra-low-dose protocol. With the standard-dose protocol, our images reconstructed from 2D projected images and 3D ground-truth CT images showed good agreement in terms of structural similarity (SSIM, 0.7675 on average), peak signal-to-noise ratio (PSNR, 25.89 dB on average), mean absolute error (MAE, 0.02364 on average), and normalized root mean square error (NRMSE, 0.05731 on average). Moreover, with the ultra-low-dose protocol, our images reconstructed from 2D projected images and the 3D ground-truth CT images also showed good agreement in terms of SSIM (0.7008 on average), PSNR (23.58 dB on average), MAE (0.02991 on average), and NRMSE (0.07349 on average).
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Affiliation(s)
- Hisaichi Shibata
- The Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
- Correspondence:
| | - Shouhei Hanaoka
- The Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Yukihiro Nomura
- The Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
- The Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
| | - Takahiro Nakao
- The Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Tomomi Takenaga
- The Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Naoto Hayashi
- The Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Osamu Abe
- The Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
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Shanker RRBJ, Zhang MH, Ginat DT. Semantic Segmentation of Extraocular Muscles on Computed Tomography Images Using Convolutional Neural Networks. Diagnostics (Basel) 2022; 12:diagnostics12071553. [PMID: 35885459 PMCID: PMC9325103 DOI: 10.3390/diagnostics12071553] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/23/2022] [Accepted: 06/24/2022] [Indexed: 11/22/2022] Open
Abstract
Computed tomography (CT) imaging of the orbit with measurement of extraocular muscle size can be useful for diagnosing and monitoring conditions that affect extraocular muscles. However, the manual measurement of extraocular muscle size can be time-consuming and tedious. The purpose of this study is to evaluate the effectiveness of deep learning algorithms in segmenting extraocular muscles and measuring muscle sizes from CT images. Consecutive CT scans of orbits from 210 patients between 1 January 2010 and 31 December 2019 were used. Extraocular muscles were manually annotated in the studies, which were then used to train the deep learning algorithms. The proposed U-net algorithm can segment extraocular muscles on coronal slices of 32 test samples with an average dice score of 0.92. The thickness and area measurements from predicted segmentations had a mean absolute error (MAE) of 0.35 mm and 3.87 mm2, respectively, with a corresponding mean absolute percentage error (MAPE) of 7 and 9%, respectively. On qualitative analysis of 32 test samples, 30 predicted segmentations from the U-net algorithm were accepted while 2 were rejected. Based on the results from quantitative and qualitative evaluation, this study demonstrates that CNN-based deep learning algorithms are effective at segmenting extraocular muscles and measuring muscles sizes.
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Affiliation(s)
| | - Michael H. Zhang
- Department of Radiology, University of Chicago, Chicago, IL 60615, USA; (R.R.B.J.S.); (M.H.Z.)
| | - Daniel T. Ginat
- Department of Radiology, Section of Neuroradiology, University of Chicago, Chicago, IL 60615, USA
- Correspondence: ; Tel.: +1-(773)-702-6039
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22
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Li S, Zeng D, Bian Z, Ma J. Noise modelling of perfusion CT images for robust hemodynamic parameter estimations. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6d9b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 05/06/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Objective. The radiation dose of cerebral perfusion computed tomography (CPCT) imaging can be reduced by lowering the milliampere-second or kilovoltage peak. However, dose reduction can decrease image quality due to excessive x-ray quanta fluctuation and reduced detector signal relative to system electronic noise, thereby influencing the accuracy of hemodynamic parameters for patients with acute stroke. Existing low-dose CPCT denoising methods, which mainly focus on specific temporal and spatial prior knowledge in low-dose CPCT images, not take the noise distribution characteristics of low-dose CPCT images into consideration. In practice, the noise of low-dose CPCT images can be much more complicated. This study first investigates the noise properties in low-dose CPCT images and proposes a perfusion deconvolution model based on the noise properties. Approach. To characterize the noise distribution in CPCT images properly, we analyze noise properties in low-dose CPCT images and find that the intra-frame noise distribution may vary in the different areas and the inter-frame noise also may vary in low-dose CPCT images. Thus, we attempt the first-ever effort to model CPCT noise with a non-independent and identical distribution (i.i.d.) mixture-of-Gaussians (MoG) model for noise assumption. Furthermore, we integrate the noise modeling strategy into a perfusion deconvolution model and present a novel perfusion deconvolution method by using self-relative structural similarity information and MoG model (named as SR-MoG) to estimate the hemodynamic parameters accurately. In the presented SR-MoG method, the self-relative structural similarity information is obtained from preprocessed low-dose CPCT images. Main results. The results show that the presented SR-MoG method can achieve promising gains over the existing deconvolution approaches. In particular, the average root-mean-square error (RMSE) of cerebral blood flow (CBF), cerebral blood volume, and mean transit time was improved by 40.3%, 69.1%, and 40.8% in the digital phantom study, and the average RMSE of CBF can be improved by 81.0% in the clinical data study, compared with tensor total variation regularization deconvolution method. Significance. The presented SR-MoG method can estimate high-accuracy hemodynamic parameters andachieve promising gains over the existing deconvolution approaches.
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He L, Lu W, Wang Z, Wang S, Xue M. CTSim: a numerical simulator of computed tomography for high-quality radiological education. Int J Comput Assist Radiol Surg 2022; 17:1257-1269. [PMID: 35622202 DOI: 10.1007/s11548-022-02656-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 04/22/2022] [Indexed: 11/30/2022]
Abstract
PURPOSE Computer-based simulation offers radiological students the possibility to experiment with computed tomography in a way not possible in class or in clinical practice. The aim of this study was to design a computed tomography numerical simulator (CTSim) for high-quality radiological education. METHODS In this study, a CTSim is designed based on the mathematical and physical principles of CT imaging. The proposed CTSim includes pen-beam module, fan-beam module, and clinical CT module. The core design of the software includes four parts: the construction of sample models, construction of imaging parameters and artifact parameters, design of data acquisition models under different scanning modes, and design of image reconstruction algorithm. After the design of the CTSim, the proposed CTSim was tested in every step of CT imaging. RESULTS Systematic verification demonstrated that the proposed CTSim can not only perform raw CT data acquisition, image reconstruction, basic image processing, and image quality analysis like a real CT scanner, but can also simulate the formation of artifacts. The CTSim can completely get rid of the hardware and achieve the same experimental results as the hardware instrument. CONCLUSION The proposed CTSim software shows several advantages such as low cost, less room accommodation, and no ionizing radiation damage and can be used as a virtual experimental training platform to enhance teaching and learning for general X-ray CT courses or for self-study of CT practitioners.
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Affiliation(s)
- Lemin He
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271016, China
| | - Weizhao Lu
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271016, China.,Medical Science and Technology Innovation Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250000, China
| | - Zhihong Wang
- School of Physics and Electronic Science, East China Normal University, Shanghai, 200241, China
| | - Shigang Wang
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271016, China
| | - Mei Xue
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271016, China.
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Wang H, Zhao X, Liu W, Li LC, Ma J, Guo L. Texture-Aware Dual Domain Mapping Model for Low Dose CT Reconstruction. Med Phys 2022; 49:3860-3873. [PMID: 35297051 DOI: 10.1002/mp.15607] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 03/10/2022] [Accepted: 03/10/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Remarkable progress has been made for low-dose CT reconstruction tasks by applying deep learning techniques. However, establishing an intrinsic link between deep learning techniques and CT texture preservation is still one of the significant challenges for researchers to further improve the effect of low dose CT reconstruction. Purpose Most of the existing deep learning-based low dose CT reconstruction methods are derived from popular frameworks, and most models focus on the image domain. Even few existing methods start with dual domains (sinogram and image) by considering the processing of the data itself, the final performances are limited due to the lack of perception of textures. With this in mind, we propose a method for texture perception on dual domains, so that the reconstruction process can be uniformly driven by visual effects. METHODS The proposed method involves the processing of two domains: the sinogram domain and the image domain. For the sinogram domain, we have designed a novel dilated residual network (S-DRN) which aims to increase the receptive field to obtain multi-scale information. For the image domain, we propose a self attention (SA) residual encoder&decoder network (SRED-Net) as the denoising network for obtaining much acceptable edges and textures. In addition, the composite loss function composed of the feature loss constructed by the proposed boundary and texture feature aware network (BTFAN) and the mean square error (MSE) can obtain a higher image quality while retaining more details and fewer artifacts, thereby obtaining better visual image quality. RESULTS The proposed method was validated using both the AAPM-Mayo clinic low-dose CT datasets and a real clinic data. Experimental results demonstrated that the new method has achieved the state-of-the-art performance on objective indicators and visual metrics in terms of denoising and texture restoration. CONCLUSIONS Compared with single-domain or existing dual-domain processing strategies, the proposed texture-aware dual domain mapping network(TADDM-Net) can much better improve the visual effect of reconstructed CT images. Meantime, we also provide much intuitive evidence in terms of model interpretability. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Huafeng Wang
- North China University of Technology, Department of Radiology, Stony Brook University, Rm 067,HSC, T8, New York, 11790, China
| | - Xuemei Zhao
- School of Information Technology, North China University of Technology, Beijing, 100041, China
| | - Wanquan Liu
- School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, 510335, China
| | - Lihong C Li
- City University of New York at College of Staten Island, Engineering and Environmental Science, Room 1N-225, 2800 Victory Blvd, Staten Island, New York, NY, 10314, USA
| | - Jianhua Ma
- Southern Medical University, Department of Biomedical Engineering, Shatai Road 1023, BAIYUN, Tonghe 1838, Guangzhou, Guangdong, 510515, China
| | - Lei Guo
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
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25
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符 帅, 李 明, 边 兆, 马 建. [Performance of low-dose CT image reconstruction for detecting intracerebral hemorrhage: selection of dose, algorithms and their combinations]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2022; 42:223-231. [PMID: 35365446 PMCID: PMC8983357 DOI: 10.12122/j.issn.1673-4254.2022.02.08] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Indexed: 06/14/2023]
Abstract
OBJECTIVE To investigate the performance of different low-dose CT image reconstruction algorithms for detecting intracerebral hemorrhage. METHODS Low-dose CT imaging simulation was performed on CT images of intracerebral hemorrhage at 30%, 25% and 20% of normal dose level (defined as 100% dose). Seven algorithms were tested to reconstruct low-dose CT images for noise suppression, including filtered back projection algorithm (FBP), penalized weighted least squares-total variation (PWLS-TV), non-local mean filter (NLM), block matching 3D (BM3D), residual encoding-decoding convolutional neural network (REDCNN), the FBP convolutional neural network (FBPConvNet) and image restoration iterative residual convolutional network (IRLNet). A deep learning-based model (CNN-LSTM) was used to detect intracerebral hemorrhage on normal dose CT images and low-dose CT images reconstructed using the 7 algorithms. The performance of different reconstruction algorithms for detecting intracerebral hemorrhage was evaluated by comparing the results between normal dose CT images and low-dose CT images. RESULTS At different dose levels, the low-dose CT images reconstructed by FBP had accuracies of detecting intracerebral hemorrhage of 82.21%, 74.61% and 65.55% at 30%, 25% and 20% dose levels, respectively. At the same dose level (30% dose), the images reconstructed by FBP, PWLS-TV, NLM, BM3D, REDCNN, FBPConvNet and IRLNet algorithms had accuracies for detecting intracerebral hemorrhage of 82.21%, 86.80%, 89.37%, 81.43%, 90.05%, 90.72% and 93.51%, respectively. The images reconstructed by IRLNet at 30%, 25% and 20% dose levels had accuracies for detecting intracerebral hemorrhage of 93.51%, 93.51% and 93.06%, respectively. CONCLUSION The performance of reconstructed low-dose CT images for detecting intracerebral hemorrhage is significantly affected by both dose and reconstruction algorithms. In clinical practice, choosing appropriate dose level and reconstruction algorithm can greatly reduce the radiation dose and ensure the detection performance of CT imaging for intracerebral hemorrhage.
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Affiliation(s)
- 帅 符
- 南方医科大学生物医学工程学院,广东 广州 510515School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
- 广州市医用放射成像与检测技术重点实验室,广东 广州 510515Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Guangzhou 510515, China
| | - 明强 李
- 琶洲实验室,广东 广州 510515Pazhou Lab, Guangzhou 510515, China
| | - 兆英 边
- 南方医科大学生物医学工程学院,广东 广州 510515School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
- 广州市医用放射成像与检测技术重点实验室,广东 广州 510515Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Guangzhou 510515, China
| | - 建华 马
- 南方医科大学生物医学工程学院,广东 广州 510515School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
- 广州市医用放射成像与检测技术重点实验室,广东 广州 510515Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Guangzhou 510515, China
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26
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Tivnan M, Wang W, Gang G, Noël P, Stayman JW. Control of Variance and Bias in CT Image Processing with Variational Training of Deep Neural Networks. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12031:120310P. [PMID: 35656120 PMCID: PMC9157378 DOI: 10.1117/12.2612417] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Optimization of CT image quality typically involves balancing noise and bias. In filtered back-projection, this trade-off is controlled by the particular filter and cutoff frequency. In penalized-likelihood iterative reconstruction, the penalty weight serves the same function. Deep neural networks typically do not provide this tuneable control over output image properties. Models are often trained to minimize mean squared error which penalizes both variance and bias in image outputs but does not offer any control over the trade-off between the two. In this work, we propose a method for controlling the output image properties of neural networks with a new loss function called weighted covariance and bias (WCB). Our proposed method includes separate weighting parameters to control the relative importance of noise or bias reduction. Moreover, we show that tuning these weights enables targeted penalization of specific image features (e.g. spatial frequencies). To evaluate our method, we present a simulation study using digital anthropormorphic phantoms, physical simulation of non-ideal CT data, and image formation with various algorithms. We show that WCB offers a greater degree of control over trade-offs between variance and bias whereas MSE has only one configuration. We also show that WCB can be used to control specific image properties including variance, bias, spatial resolution, and the noise correlation of neural network outputs. Finally, we present a method to optimize the proposed weights for stimulus detectability. Our results demonstrate the potential for this new capability to control the image properties of DNN outputs and optimize image quality for the task-specific applications.
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Affiliation(s)
- Matthew Tivnan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
| | - Wenying Wang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
| | - Grace Gang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
| | - Peter Noël
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA
| | - J Webster Stayman
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
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27
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Zeng D, Wang L, Geng M, Li S, Deng Y, Xie Q, Li D, Zhang H, Li Y, Xu Z, Meng D, Ma J. Noise-Generating-Mechanism-Driven Unsupervised Learning for Low-Dose CT Sinogram Recovery. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022. [DOI: 10.1109/trpms.2021.3083361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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28
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Tao X, Wang Y, Lin L, Hong Z, Ma J. Learning to Reconstruct CT Images From the VVBP-Tensor. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3030-3041. [PMID: 34138703 DOI: 10.1109/tmi.2021.3090257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Deep learning (DL) is bringing a big movement in the field of computed tomography (CT) imaging. In general, DL for CT imaging can be applied by processing the projection or the image data with trained deep neural networks (DNNs), unrolling the iterative reconstruction as a DNN for training, or training a well-designed DNN to directly reconstruct the image from the projection. In all of these applications, the whole or part of the DNNs work in the projection or image domain alone or in combination. In this study, instead of focusing on the projection or image, we train DNNs to reconstruct CT images from the view-by-view backprojection tensor (VVBP-Tensor). The VVBP-Tensor is the 3D data before summation in backprojection. It contains structures of the scanned object after applying a sorting operation. Unlike the image or projection that provides compressed information due to the integration/summation step in forward or back projection, the VVBP-Tensor provides lossless information for processing, allowing the trained DNNs to preserve fine details of the image. We develop a learning strategy by inputting slices of the VVBP-Tensor as feature maps and outputting the image. Such strategy can be viewed as a generalization of the summation step in conventional filtered backprojection reconstruction. Numerous experiments reveal that the proposed VVBP-Tensor domain learning framework obtains significant improvement over the image, projection, and hybrid projection-image domain learning frameworks. We hope the VVBP-Tensor domain learning framework could inspire algorithm development for DL-based CT imaging.
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29
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30
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Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer. J Digit Imaging 2021; 33:504-515. [PMID: 31515756 DOI: 10.1007/s10278-019-00274-4] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Low-dose CT denoising is a challenging task that has been studied by many researchers. Some studies have used deep neural networks to improve the quality of low-dose CT images and achieved fruitful results. In this paper, we propose a deep neural network that uses dilated convolutions with different dilation rates instead of standard convolution helping to capture more contextual information in fewer layers. Also, we have employed residual learning by creating shortcut connections to transmit image information from the early layers to later ones. To further improve the performance of the network, we have introduced a non-trainable edge detection layer that extracts edges in horizontal, vertical, and diagonal directions. Finally, we demonstrate that optimizing the network by a combination of mean-square error loss and perceptual loss preserves many structural details in the CT image. This objective function does not suffer from over smoothing and blurring effects causing by per-pixel loss and grid-like artifacts resulting from perceptual loss. The experiments show that each modification to the network improves the outcome while changing the complexity of the network, minimally.
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31
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Kulathilake KASH, Abdullah NA, Sabri AQM, Lai KW. A review on Deep Learning approaches for low-dose Computed Tomography restoration. COMPLEX INTELL SYST 2021; 9:2713-2745. [PMID: 34777967 PMCID: PMC8164834 DOI: 10.1007/s40747-021-00405-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 05/18/2021] [Indexed: 02/08/2023]
Abstract
Computed Tomography (CT) is a widely use medical image modality in clinical medicine, because it produces excellent visualizations of fine structural details of the human body. In clinical procedures, it is desirable to acquire CT scans by minimizing the X-ray flux to prevent patients from being exposed to high radiation. However, these Low-Dose CT (LDCT) scanning protocols compromise the signal-to-noise ratio of the CT images because of noise and artifacts over the image space. Thus, various restoration methods have been published over the past 3 decades to produce high-quality CT images from these LDCT images. More recently, as opposed to conventional LDCT restoration methods, Deep Learning (DL)-based LDCT restoration approaches have been rather common due to their characteristics of being data-driven, high-performance, and fast execution. Thus, this study aims to elaborate on the role of DL techniques in LDCT restoration and critically review the applications of DL-based approaches for LDCT restoration. To achieve this aim, different aspects of DL-based LDCT restoration applications were analyzed. These include DL architectures, performance gains, functional requirements, and the diversity of objective functions. The outcome of the study highlights the existing limitations and future directions for DL-based LDCT restoration. To the best of our knowledge, there have been no previous reviews, which specifically address this topic.
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Affiliation(s)
- K. A. Saneera Hemantha Kulathilake
- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Nor Aniza Abdullah
- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Aznul Qalid Md Sabri
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
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32
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Li S, Zeng D, Bian Z, Li D, Zhu M, Huang J, Ma J. Learning non-local perfusion textures for high-quality computed tomography perfusion imaging. Phys Med Biol 2021; 66. [PMID: 33910178 DOI: 10.1088/1361-6560/abfc90] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 04/28/2021] [Indexed: 11/11/2022]
Abstract
Background. Computed tomography perfusion (CTP) imaging plays a critical role in the acute stroke syndrome assessment due to its widespread availability, speed of image acquisition, and relatively low cost. However, due to its repeated scanning protocol, CTP imaging involves a substantial radiation dose, which might increase potential cancer risks.Methods. In this work, we present a novel deep learning model called non-local perfusion texture learning network (NPTN) for high-quality CTP imaging at low-dose cases. Specifically, considering abundant similarities in the CTP images, i.e. latent self-similarities within the non-local region in the CTP images, we firstly search the most similar pixels from the adjacent frames within a fixed search window to obtain the non-local similarities and to construct non-local textures vector. Then, both the low-dose frame and these non-local textures from adjacent frames are fed into a convolution neural network to predict high-quality CTP images, which can help better characterize the structure details and contrast variants in the targeted CTP image rather than simply utilizing the targeted frame itself. The residual learning strategy and batch normalization are utilized to boost the performance of the convolution neural network. In the experiment, the CTP images of 31 patients with suspected stroke disease are collected to demonstrate the performance of the presented NPTN method.Results. The results show the presented NPTN method obtains superior performance compared with the competing methods. From numerical value, at all dose levels, the presented NPTN method has achieved around 3.0 dB improvement of average PSNR, an increase of around 1.4% of average SSIM, and a decrease of around 4.8% of average RMSE in the low-dose CTP reconstruction task, and also has achieved an increase of around 3.4% of average SSIM and a decrease of around 61.1% of average RMSE in the cerebral blood flow (CBF) estimation task.Conclusions. The presented NPTN method can obtain high-quality CTP images and estimate high-accuracy CBF map by characterizing more structure details and contrast variants in the CTP image and outperform the competing methods at low-dose cases.
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Affiliation(s)
- Sui Li
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Dong Zeng
- College of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, People's Republic of China.,Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou 510335, People's Republic of China
| | - Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Danyang Li
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Manman Zhu
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Jing Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China.,Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, People's Republic of China.,Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou 510335, People's Republic of China
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33
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Weakly-supervised progressive denoising with unpaired CT images. Med Image Anal 2021; 71:102065. [PMID: 33915472 DOI: 10.1016/j.media.2021.102065] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/16/2021] [Accepted: 03/30/2021] [Indexed: 12/12/2022]
Abstract
Although low-dose CT imaging has attracted a great interest due to its reduced radiation risk to the patients, it suffers from severe and complex noise. Recent fully-supervised methods have shown impressive performances on CT denoising task. However, they require a huge amount of paired normal-dose and low-dose CT images, which is generally unavailable in real clinical practice. To address this problem, we propose a weakly-supervised denoising framework that generates paired original and noisier CT images from unpaired CT images using a physics-based noise model. Our denoising framework also includes a progressive denoising module that bypasses the challenges of mapping from low-dose to normal-dose CT images directly via progressively compensating the small noise gap. To quantitatively evaluate diagnostic image quality, we present the noise power spectrum and signal detection accuracy, which are well correlated with the visual inspection. The experimental results demonstrate that our method achieves remarkable performances, even superior to fully-supervised CT denoising with respect to the signal detectability. Moreover, our framework increases the flexibility in data collection, allowing us to utilize any unpaired data at any dose levels.
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34
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Shiri I, Akhavanallaf A, Sanaat A, Salimi Y, Askari D, Mansouri Z, Shayesteh SP, Hasanian M, Rezaei-Kalantari K, Salahshour A, Sandoughdaran S, Abdollahi H, Arabi H, Zaidi H. Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network. Eur Radiol 2021; 31:1420-1431. [PMID: 32879987 PMCID: PMC7467843 DOI: 10.1007/s00330-020-07225-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 08/13/2020] [Accepted: 08/21/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVES The current study aimed to design an ultra-low-dose CT examination protocol using a deep learning approach suitable for clinical diagnosis of COVID-19 patients. METHODS In this study, 800, 170, and 171 pairs of ultra-low-dose and full-dose CT images were used as input/output as training, test, and external validation set, respectively, to implement the full-dose prediction technique. A residual convolutional neural network was applied to generate full-dose from ultra-low-dose CT images. The quality of predicted CT images was assessed using root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Scores ranging from 1 to 5 were assigned reflecting subjective assessment of image quality and related COVID-19 features, including ground glass opacities (GGO), crazy paving (CP), consolidation (CS), nodular infiltrates (NI), bronchovascular thickening (BVT), and pleural effusion (PE). RESULTS The radiation dose in terms of CT dose index (CTDIvol) was reduced by up to 89%. The RMSE decreased from 0.16 ± 0.05 to 0.09 ± 0.02 and from 0.16 ± 0.06 to 0.08 ± 0.02 for the predicted compared with ultra-low-dose CT images in the test and external validation set, respectively. The overall scoring assigned by radiologists showed an acceptance rate of 4.72 ± 0.57 out of 5 for reference full-dose CT images, while ultra-low-dose CT images rated 2.78 ± 0.9. The predicted CT images using the deep learning algorithm achieved a score of 4.42 ± 0.8. CONCLUSIONS The results demonstrated that the deep learning algorithm is capable of predicting standard full-dose CT images with acceptable quality for the clinical diagnosis of COVID-19 positive patients with substantial radiation dose reduction. KEY POINTS • Ultra-low-dose CT imaging of COVID-19 patients would result in the loss of critical information about lesion types, which could potentially affect clinical diagnosis. • Deep learning-based prediction of full-dose from ultra-low-dose CT images for the diagnosis of COVID-19 could reduce the radiation dose by up to 89%. • Deep learning algorithms failed to recover the correct lesion structure/density for a number of patients considered outliers, and as such, further research and development is warranted to address these limitations.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Azadeh Akhavanallaf
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Amirhossein Sanaat
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Dariush Askari
- Department of Radiology Technology, Shahid Beheshti University of Medical, Tehran, Iran
| | - Zahra Mansouri
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sajad P Shayesteh
- Department of Physiology, Pharmacology and Medical Physics, Alborz University of Medical Sciences, Karaj, Iran
| | - Mohammad Hasanian
- Department of Radiology, Arak University of Medical Sciences, Arak, Iran
| | - Kiara Rezaei-Kalantari
- Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Science, Tehran, Iran
| | - Ali Salahshour
- Department of Radiology, Alborz University of Medical Sciences, Karaj, Iran
| | - Saleh Sandoughdaran
- Department of Radiation Oncology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Sciences and Medical Physics, Faculty of Allied Medicine, Kerman University of Medical sciences, Kerman, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.
- Geneva University Neurocenter, Geneva University, CH-1205, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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高 琦, 朱 曼, 李 丹, 边 兆, 马 建. [CT image quality assessment based on prior information of pre-restored images]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2021; 41:230-237. [PMID: 33624596 PMCID: PMC7905247 DOI: 10.12122/j.issn.1673-4254.2021.02.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Indexed: 06/12/2023]
Abstract
OBJECTIVE We propose a CT IQA strategy based on the prior information of pre-restored images (PR-IQA) to improve the performance of IQA models. OBJECTIVE We propose a CNN-based no-reference CT IQA strategy using the prior information of image quality features in the image restoration algorithm, which is combined with the original distorted image information into the two CNNs through the pre-restored image and the residual image. Multi-information fusion was used to improve the feature extraction ability and prediction performance of CNN. We built a CT IQA dataset based on spiral CT data published by Mayo Clinic. The performance of PR- IQA was evaluated by calculating the quantitative metrics and statistical tests. The influence of different hyperparameter settings for PR-IQA was analyzed. We then compared PR-IQA with the BASELINE model based on the single CNN to evaluate the original distorted image without reference image and other eight IQA algorithms. OBJECTIVE The comparative experiment results showed that the PR-IQA model based on the prior information of 3 different image restoration algorithms (BF, NLM and BM3D) was better than all the tested IQA algorithms. Compared with the BASELINE method, the proposed method showed significantly improved performance, and the mean PLCC was increased by 12.56% and SROCC by 19.95%, and RMSE was decreased by 22.77%. OBJECTIVE The proposed PR-IQA method can make full use of the prior information of the image restoration algorithm to effectively predict the quality of CT images.
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Affiliation(s)
- 琦 高
- />南方医科大学生物医学工程学院//广州市医用放射成像与检测技术重点实验室,广东 广州 510515School of Biomedical Engineering, Southern Medical University; Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Guangzhou 510515, China
| | - 曼曼 朱
- />南方医科大学生物医学工程学院//广州市医用放射成像与检测技术重点实验室,广东 广州 510515School of Biomedical Engineering, Southern Medical University; Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Guangzhou 510515, China
| | - 丹阳 李
- />南方医科大学生物医学工程学院//广州市医用放射成像与检测技术重点实验室,广东 广州 510515School of Biomedical Engineering, Southern Medical University; Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Guangzhou 510515, China
| | - 兆英 边
- />南方医科大学生物医学工程学院//广州市医用放射成像与检测技术重点实验室,广东 广州 510515School of Biomedical Engineering, Southern Medical University; Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Guangzhou 510515, China
| | - 建华 马
- />南方医科大学生物医学工程学院//广州市医用放射成像与检测技术重点实验室,广东 广州 510515School of Biomedical Engineering, Southern Medical University; Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Guangzhou 510515, China
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Zhang H, Liu B, Yu H, Dong B. MetaInv-Net: Meta Inversion Network for Sparse View CT Image Reconstruction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:621-634. [PMID: 33104506 DOI: 10.1109/tmi.2020.3033541] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
X-ray Computed Tomography (CT) is widely used in clinical applications such as diagnosis and image-guided interventions. In this paper, we propose a new deep learning based model for CT image reconstruction with the backbone network architecture built by unrolling an iterative algorithm. However, unlike the existing strategy to include as many data-adaptive components in the unrolled dynamics model as possible, we find that it is enough to only learn the parts where traditional designs mostly rely on intuitions and experience. More specifically, we propose to learn an initializer for the conjugate gradient (CG) algorithm that involved in one of the subproblems of the backbone model. Other components, such as image priors and hyperparameters, are kept as the original design. Since a hypernetwork is introduced to inference on the initialization of the CG module, it makes the proposed model a certain meta-learning model. Therefore, we shall call the proposed model the meta-inversion network (MetaInv-Net). The proposed MetaInv-Net can be designed with much less trainable parameters while still preserves its superior image reconstruction performance than some state-of-the-art deep models in CT imaging. In simulated and real data experiments, MetaInv-Net performs very well and can be generalized beyond the training setting, i.e., to other scanning settings, noise levels, and data sets.
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Li D, Zeng D, Li S, Ge Y, Bian Z, Huang J, Ma J. MDM-PCCT: Multiple Dynamic Modulations for High-Performance Spectral PCCT Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:3630-3642. [PMID: 32746110 DOI: 10.1109/tmi.2020.3001616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Photon counting computed tomography (PCCT) has the ability to identify individual photons, resulting in quantitative material identification. Meanwhile, several technical challenges still exist in current PCCT imaging systems, including increased noise and suboptimal bin selection. These nonideal effects can substantially degrade the reconstruction performance and material estimation accuracy. To address these issues, in this work, we present a novel system for high-performance spectral PCCT imaging, which is a combination of multiple dynamic modulations, interpolation-based measurements processing strategy and advanced reconstruction method. For simplicity, this new PCCT imaging system is referred to as "MDM-PCCT". Specifically, the multiple dynamic modulations consist of dynamic kVp modulation, dynamic spectrum modulation and dynamic energy threshold modulation. In the dynamic kVp modulation, three kVp values, i.e., 80, 110 and 140, are included, and the tube voltage waveform follows a sinusoidal curve which is more practical than the rectangular curve in the fast kV switching mode. In the dynamic spectrum modulation, the X-ray spectra are processed by selective spatial-spectral filters to balance the X-ray fluxes and increase the spectral separation. In the dynamic energy threshold modulation, the energy threshold is adaptively changed to determine the optimal bin selection. Furthermore, we propose an energy threshold determination method and interpolation-based measurements processing strategy to address the issue of non-uniform and sparse-view PCCT measurements, respectively. In addition, by considering the intrinsic characteristics of the MDM-PCCT images, we utilize an enhanced total variation regularized model for images reconstruction. Finally, numerical and preclinical studies demonstrate that the presented MDM-PCCT imaging system is capable of yielding uniform and high-fidelity PCCT measurements with noise consistency, and the presented reconstruction method further improves the image quality and material decomposition accuracy.
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Sheng J, Chen B, Ma Y, Shi Y. A novel reconstruction approach combining OSEM and split Bregman method for low dose CT. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Zeng D, Yao L, Ge Y, Li S, Xie Q, Zhang H, Bian Z, Zhao Q, Li Y, Xu Z, Meng D, Ma J. Full-Spectrum-Knowledge-Aware Tensor Model for Energy-Resolved CT Iterative Reconstruction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2831-2843. [PMID: 32112677 DOI: 10.1109/tmi.2020.2976692] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Energy-resolved computed tomography (ErCT) with a photon counting detector concurrently produces multiple CT images corresponding to different photon energy ranges. It has the potential to generate energy-dependent images with improved contrast-to-noise ratio and sufficient material-specific information. Since the number of detected photons in one energy bin in ErCT is smaller than that in conventional energy-integrating CT (EiCT), ErCT images are inherently more noisy than EiCT images, which leads to increased noise and bias in the subsequent material estimation. In this work, we first deeply analyze the intrinsic tensor properties of two-dimensional (2D) ErCT images acquired in different energy bins and then present a F ull- S pectrum-knowledge-aware Tensor analysis and processing (FSTensor) method for ErCT reconstruction to suppress noise-induced artifacts to obtain high-quality ErCT images and high-accuracy material images. The presented method is based on three considerations: (1) 2D ErCT images obtained in different energy bins can be treated as a 3-order tensor with three modes, i.e., width, height and energy bin, and a rich global correlation exists among the three modes, which can be characterized by tensor decomposition. (2) There is a locally piecewise smooth property in the 3-order ErCT images, and it can be captured by a tensor total variation regularization. (3) The images from the full spectrum are much better than the ErCT images with respect to noise variance and structural details and serve as external information to improve the reconstruction performance. We then develop an alternating direction method of multipliers algorithm to numerically solve the presented FSTensor method. We further utilize a genetic algorithm to tackle the parameter selection in ErCT reconstruction, instead of manually determining parameters. Simulation, preclinical and synthesized clinical ErCT results demonstrate that the presented FSTensor method leads to significant improvements over the filtered back-projection, robust principal component analysis, tensor-based dictionary learning and low-rank tensor decomposition with spatial-temporal total variation methods.
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Gao Y, Liang Z, Xing Y, Zhang H, Pomeroy M, Lu S, Ma J, Lu H, Moore W. Characterization of tissue-specific pre-log Bayesian CT reconstruction by texture-dose relationship. Med Phys 2020; 47:5032-5047. [PMID: 32786070 DOI: 10.1002/mp.14449] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 06/21/2020] [Accepted: 08/04/2020] [Indexed: 12/18/2022] Open
Abstract
PURPOSE Tissue textures have been recognized as biomarkers for various clinical tasks. In computed tomography (CT) image reconstruction, it is important but challenging to preserve the texture when lowering x-ray exposure from full- toward low-/ultra-low dose level. Therefore, this paper aims to explore the texture-dose relationship within one tissue-specific pre-log Bayesian CT reconstruction algorithm. METHODS To enhance the texture in ultra-low dose CT (ULdCT) reconstruction, this paper presents a Bayesian type algorithm. A shifted Poisson model is adapted to describe the statistical properties of pre-log data, and a tissue-specific Markov random field prior (MRFt) is used to incorporate tissue texture from previous full-dose CT, thus called SP-MRFt algorithm. Utilizing the SP-MRFt algorithm, we investigated tissue texture degradation as a function of x-ray dose levels from full dose (100 mAs/120 kVp) to ultralow dose (1 mAs/120 kVp) by using quantitative texture-based evaluation metrics. RESULTS Experimental results show the SP-MRFt algorithm outperforms conventional filtered back projection (FBP) and post-log domain penalized weighted least square MRFt (PWLS-MRFt) in terms of noise suppression and texture preservation. Comparable results are also obtained with shifted Poisson model with 7 × 7 Huber MRF weights (SP-Huber7). The investigation on texture-dose relationship shows that the quantified texture measures drop monotonically as dose level decreases, and interestingly a turning point is observed on the texture-dose response curve. CONCLUSIONS This important observation implies that there exists a minimum dose level, at which a given CT scanner (hardware configuration and image reconstruction software) can achieve without compromising clinical tasks. Moreover, the experiment results show that the variance of electronic noise has higher impact than the mean to the texture-dose relationship.
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Affiliation(s)
- Yongfeng Gao
- Department of Radiology, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Zhengrong Liang
- Departments of Radiology, Biomedical Engineering, Computer Science, and Electrical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Yuxiang Xing
- Department of Engineering Physics, Tsinghua University, Beijing, 100871, China
| | - Hao Zhang
- Departments of Radiology and Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Marc Pomeroy
- Departments of Radiology and Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY, 11794, USA
| | - Siming Lu
- Departments of Radiology and Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY, 11794, USA
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Hongbing Lu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China
| | - William Moore
- Department of Radiology, New York University, New York, NY, 10016, USA
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Elhamiasl M, Nuyts J. Low-dose x-ray CT simulation from an available higher-dose scan. ACTA ACUST UNITED AC 2020; 65:135010. [DOI: 10.1088/1361-6560/ab8953] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Gao Y, Tan J, Shi Y, Lu S, Gupta A, Li H, Liang Z. Constructing a tissue-specific texture prior by machine learning from previous full-dose scan for Bayesian reconstruction of current ultralow-dose CT images. J Med Imaging (Bellingham) 2020; 7:032502. [PMID: 32118093 PMCID: PMC7040436 DOI: 10.1117/1.jmi.7.3.032502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 01/27/2020] [Indexed: 11/14/2022] Open
Abstract
Purpose: Bayesian theory provides a sound framework for ultralow-dose computed tomography (ULdCT) image reconstruction with two terms for modeling the data statistical property and incorporating a priori knowledge for the image that is to be reconstructed. We investigate the feasibility of using a machine learning (ML) strategy, particularly the convolutional neural network (CNN), to construct a tissue-specific texture prior from previous full-dose computed tomography. Approach: Our study constructs four tissue-specific texture priors, corresponding with lung, bone, fat, and muscle, and integrates the prior with the prelog shift Poisson (SP) data property for Bayesian reconstruction of ULdCT images. The Bayesian reconstruction was implemented by an algorithm called SP-CNN-T and compared with our previous Markov random field (MRF)-based tissue-specific texture prior algorithm called SP-MRF-T. Results: In addition to conventional quantitative measures, mean squared error and peak signal-to-noise ratio, structure similarity index, feature similarity, and texture Haralick features were used to measure the performance difference between SP-CNN-T and SP-MRF-T algorithms in terms of the structure and tissue texture preservation, demonstrating the feasibility and the potential of the investigated ML approach. Conclusions: Both training performance and image reconstruction results showed the feasibility of constructing CNN texture prior model and the potential of improving the structure preservation of the nodule comparing to our previous regional tissue-specific MRF texture prior model.
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Affiliation(s)
- Yongfeng Gao
- State University of New York, Department of Radiology, Stony Brook, New York, United States
| | - Jiaxing Tan
- State University of New York, Department of Radiology, Stony Brook, New York, United States
| | - Yongyi Shi
- State University of New York, Department of Radiology, Stony Brook, New York, United States
| | - Siming Lu
- State University of New York, Department of Radiology, Stony Brook, New York, United States
- State University of New York, Department of Biomedical Engineering, Stony Brook, New York, United States
| | - Amit Gupta
- State University of New York, Department of Radiology, Stony Brook, New York, United States
| | - Haifang Li
- State University of New York, Department of Radiology, Stony Brook, New York, United States
| | - Zhengrong Liang
- State University of New York, Department of Radiology, Stony Brook, New York, United States
- State University of New York, Department of Biomedical Engineering, Stony Brook, New York, United States
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Tao X, Zhang H, Wang Y, Yan G, Zeng D, Chen W, Ma J. VVBP-Tensor in the FBP Algorithm: Its Properties and Application in Low-Dose CT Reconstruction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:764-776. [PMID: 31425024 DOI: 10.1109/tmi.2019.2935187] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
For decades, commercial X-ray computed tomography (CT) scanners have been using the filtered backprojection (FBP) algorithm for image reconstruction. However, the desire for lower radiation doses has pushed the FBP algorithm to its limit. Previous studies have made significant efforts to improve the results of FBP through preprocessing the sinogram, modifying the ramp filter, or postprocessing the reconstructed images. In this paper, we focus on analyzing and processing the stacked view-by-view backprojections (named VVBP-Tensor) in the FBP algorithm. A key challenge for our analysis lies in the radial structures in each backprojection slice. To overcome this difficulty, a sorting operation was introduced to the VVBP-Tensor in its z direction (the direction of the projection views). The results show that, after sorting, the tensor contains structures that are similar to those of the object, and structures in different slices of the tensor are correlated. We then analyzed the properties of the VVBP-Tensor, including structural self-similarity, tensor sparsity, and noise statistics. Considering these properties, we have developed an algorithm using the tensor singular value decomposition (named VVBP-tSVD) to denoise the VVBP-Tensor for low-mAs CT imaging. Experiments were conducted using a physical phantom and clinical patient data with different mAs levels. The results demonstrate that the VVBP-tSVD is superior to all competing methods under different reconstruction schemes, including sinogram preprocessing, image postprocessing, and iterative reconstruction. We conclude that the VVBP-Tensor is a suitable processing target for improving the quality of FBP reconstruction, and the proposed VVBP-tSVD is an effective algorithm for noise reduction in low-mAs CT imaging. This preliminary work might provide a heuristic perspective for reviewing and rethinking the FBP algorithm.
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Yao L, Zeng D, Chen G, Liao Y, Li S, Zhang Y, Wang Y, Tao X, Niu S, Lv Q, Bian Z, Ma J, Huang J. Multi-energy computed tomography reconstruction using a nonlocal spectral similarity model. Phys Med Biol 2019; 64:035018. [PMID: 30577033 DOI: 10.1088/1361-6560/aafa99] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Multi-energy computed tomography (MECT) is able to acquire simultaneous multi-energy measurements from one scan. In addition, it allows material differentiation and quantification effectively. However, due to the limited energy bin width, the number of photons detected in an energy-specific channel is smaller than that in traditional CT, which results in image quality degradation. To address this issue, in this work, we develop a statistical iterative reconstruction algorithm to acquire high-quality MECT images and high-accuracy material-specific images. Specifically, this algorithm fully incorporates redundant self-similarities within nonlocal regions in the MECT image at one bin and rich spectral similarities among MECT images at all bins. For simplicity, the presented algorithm is referred to as 'MECT-NSS'. Moreover, an efficient optimization algorithm is developed to solve the MECT-NSS objective function. Then, a comprehensive evaluation of parameter selection for the MECT-NSS algorithm is conducted. In the experiment, the datasets include images from three phantoms and one patient to validate and evaluate the MECT-NSS reconstruction performance. The qualitative and quantitative results demonstrate that the presented MECT-NSS can successfully yield better MECT image quality and more accurate material estimation than the competing algorithms.
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Affiliation(s)
- Lisha Yao
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China. Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangzhou 510515, People's Republic of China. These authors contributed equally
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Jia X, Liao Y, Zeng D, Zhang H, Zhang Y, He J, Bian Z, Wang Y, Tao X, Liang Z, Huang J, Ma J. Statistical CT reconstruction using region-aware texture preserving regularization learning from prior normal-dose CT image. Phys Med Biol 2018; 63:225020. [PMID: 30457116 PMCID: PMC6309620 DOI: 10.1088/1361-6560/aaebc9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
In some clinical applications, prior normal-dose CT (NdCT) images are available, and the valuable textures and structure features in them may be used to promote follow-up low-dose CT (LdCT) reconstruction. This study aims to learn texture information from the NdCT images and leverage it for follow-up LdCT image reconstruction to preserve textures and structure features. Specifically, the proposed reconstruction method first learns the texture information from those patches with similar structures in NdCT image, and the similar patches can be clustered by searching context features efficiently from the surroundings of the current patch. Then it utilizes redundant texture information from the similar patches as a priori knowledge to describe specific regions in the LdCT image. The advanced region-aware texture preserving prior is termed as 'RATP'. The main advantage of the PATP prior is that it can properly learn the texture features from available NdCT images and adaptively characterize the region-specific structures in the LdCT image. The experiments using patient data were performed to evaluate the performance of the proposed method. The proposed RATP method demonstrated superior performance in LdCT imaging compared to the filtered back projection (FBP) and statistical iterative reconstruction (SIR) methods using Gaussian regularization, Huber regularization and the original texture preserving regularization.
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Affiliation(s)
- Xiao Jia
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People’s Republic of China
- School of Software Engineering, Nanyang Normal University, Nanyang, Henan 473061, People’s Republic of China
- Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangzhou, Guangdong 510515, People’s Republic of China
| | - Yuting Liao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People’s Republic of China
- Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangzhou, Guangdong 510515, People’s Republic of China
| | - Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People’s Republic of China
- Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangzhou, Guangdong 510515, People’s Republic of China
| | - Hao Zhang
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, United States of America
| | - Yuanke Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People’s Republic of China
- Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangzhou, Guangdong 510515, People’s Republic of China
| | - Ji He
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People’s Republic of China
- Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangzhou, Guangdong 510515, People’s Republic of China
| | - Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People’s Republic of China
- Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangzhou, Guangdong 510515, People’s Republic of China
| | - Yongbo Wang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People’s Republic of China
- Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangzhou, Guangdong 510515, People’s Republic of China
| | - Xi Tao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People’s Republic of China
- Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangzhou, Guangdong 510515, People’s Republic of China
| | - Zhengrong Liang
- Department of Radiology and Biomedical Engineering, State University of New York at Stony Brook, NY 11794, United States of America
| | - Jing Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People’s Republic of China
- Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangzhou, Guangdong 510515, People’s Republic of China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People’s Republic of China
- Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangzhou, Guangdong 510515, People’s Republic of China
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Wang Y, Liao Y, Zhang Y, He J, Li S, Bian Z, Zhang H, Gao Y, Meng D, Zuo W, Zeng D, Ma J. Iterative quality enhancement via residual-artifact learning networks for low-dose CT. Phys Med Biol 2018; 63:215004. [PMID: 30265251 DOI: 10.1088/1361-6560/aae511] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Radiation exposure and the associated risk of cancer for patients in computed tomography (CT) scans have been major clinical concerns. The radiation exposure can be reduced effectively via lowering the x-ray tube current (mA). However, this strategy may lead to excessive noise and streak artifacts in the conventional filtered back-projection reconstructed images. To address this issue, some deep convolutional neural network (ConvNet) based approaches have been developed for low-dose CT imaging inspired by the recent development of machine learning. Nevertheless, some of the image textures reconstructed by the ConvNet could be corrupted by the severe streaks, especially in ultra-low-dose cases, which could be close to prostheses and hamper diagnosis. Therefore, in this work, we propose an iterative residual-artifact learning ConvNet (IRLNet) approach to improve the reconstruction performance over the ConvNet based approaches. Specifically, the proposed IRLNet estimates the high-frequency details within the noise and then removes them iteratively; after eliminating severe streaks in the low-dose CT images, the residual low-frequency details can be processed through the conventional network. Moreover, the proposed IRLNet scheme can be extended for robust handling of quantitative dual energy CT/cerebral perfusion CT imaging, and statistical iterative reconstruction. Real patient data are used to evaluate the proposed IRLNet, and the experimental results demonstrate that the proposed IRLNet approach outperforms the previous ConvNet based approaches in reducing the image noise and streak artifacts efficiently at the same time as preserving edge details well, suggesting that the proposed IRLNet approach can be used to improve the CT image quality, especially in ultra-low-dose cases.
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Affiliation(s)
- Yongbo Wang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China. Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China. These authors contributed equally
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Image based simulation of the low dose computed tomography images suggests 13 mAs 120 kV suitability for non-syndromic craniosynostosis diagnosis without iterative reconstruction algorithms. Eur J Radiol 2018; 105:168-174. [DOI: 10.1016/j.ejrad.2018.06.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 05/03/2018] [Accepted: 06/10/2018] [Indexed: 11/18/2022]
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Zhang H, Ma J, Wang J, Moore W, Liang Z. Assessment of prior image induced nonlocal means regularization for low-dose CT reconstruction: Change in anatomy. Med Phys 2018; 44:e264-e278. [PMID: 28901622 DOI: 10.1002/mp.12378] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 05/04/2017] [Accepted: 05/18/2017] [Indexed: 11/06/2022] Open
Abstract
PURPOSE Repeated computed tomography (CT) scans are prescribed for some clinical applications such as lung nodule surveillance. Several studies have demonstrated that incorporating a high-quality prior image into the reconstruction of subsequent low-dose CT (LDCT) acquisitions can either improve image quality or reduce data fidelity requirements. Our proposed previous normal-dose image induced nonlocal means (ndiNLM) regularization method for LDCT is an example of such a method. However, one major concern with prior image based methods is that they might produce false information when the prior image and the current LDCT image show different structures (for example, if a lung nodule emerges, grows, shrinks, or disappears over time). This study aims to assess the performance of the ndiNLM regularization method in situations with change in anatomy. METHOD We incorporated the ndiNLM regularization into the statistical image reconstruction (SIR) framework for reconstruction of subsequent LDCT images. Because of its patch-based search mechanism, a rough registration between the prior image and the current LDCT image is adequate for the SIR-ndiNLM method. We assessed the performance of the SIR-ndiNLM method in lung nodule surveillance for two different scenarios: (a) the nodule was not found in a baseline exam but appears in a follow-up LDCT scan; (b) the nodule was present in a baseline exam but disappears in a follow-up LDCT scan. We further investigated the effect of nodule size on the performance of the SIR-ndiNLM method. RESULTS We found that a relatively large search-window (e.g., 33 × 33) should be used for the SIR-ndiNLM method to account for misalignment between the prior image and the current LDCT image, and to ensure that enough similar patches can be found in the prior image. With proper selection of other parameters, experimental results with two patient datasets demonstrated that the SIR-ndiNLM method did not miss true nodules nor introduce false nodules in the lung nodule surveillance scenarios described above. We also found that the SIR-ndiNLM reconstruction shows improved image quality when the prior image is similar to the current LDCT image in anatomy. These gains in image quality might appear small upon visual inspection, but they can be detected using quantitative measures. Finally, the SIR-ndiNLM method also performed well in ultra-low-dose conditions and with different nodule sizes. CONCLUSIONS This study assessed the performance of the SIR-ndiNLM method in situations in which the prior image and the current LDCT image show substantial anatomical differences, specifically, changes in lung nodules. The experimental results demonstrate that the SIR-ndiNLM method does not introduce false lung nodules nor miss true nodules, which relieves the concern that this method might produce false information. However, there is insufficient evidence that these findings will hold true for all kinds of anatomical changes.
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Affiliation(s)
- Hao Zhang
- Department of Radiology, Stony Brook University, NY, 11794, USA.,Department of Biomedical Engineering, Stony Brook University, NY, 11794, USA
| | - Jianhua Ma
- Department of Biomedical Engineering, Southern Medical University, Guangdong, 510515, China
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, TX, 75390, USA
| | - William Moore
- Department of Radiology, Stony Brook University, NY, 11794, USA
| | - Zhengrong Liang
- Department of Radiology, Stony Brook University, NY, 11794, USA.,Department of Biomedical Engineering, Stony Brook University, NY, 11794, USA
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49
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Bian Z, Zeng D, Zhang Z, Gong C, Tian X, Yan G, Huang J, Guo H, Chen B, Zhang J, Feng Q, Chen W, Ma J. Low-dose dynamic myocardial perfusion CT imaging using a motion adaptive sparsity prior. Med Phys 2018; 44:e188-e201. [PMID: 28901610 DOI: 10.1002/mp.12285] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2016] [Revised: 02/20/2017] [Accepted: 04/09/2017] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Dynamic myocardial perfusion computed tomography (DM-PCT) imaging offers benefits over quantitative assessment of myocardial blood flow (MBF) for diagnosis and risk stratification of coronary artery disease. However, one major drawback of DM-PCT imaging is that a high radiation level is imparted by repeated scanning. To address this issue, in this work, we developed a statistical iterative reconstruction algorithm based on the penalized weighted least-squares (PWLS) scheme by incorporating a motion adaptive sparsity prior (MASP) model to achieve high-quality DM-PCT imaging with low tube current dynamic data acquisition. For simplicity, we refer to the proposed algorithm as "PWLS-MASP''. METHODS The MASP models both the spatial and temporal structured sparsity of DM-PCT sequence images with the assumption that the differences between adjacent frames after motion correction are sparse in the gradient image domain. To validate and evaluate the effectiveness of the present PWLS-MASP algorithm thoroughly, a modified XCAT phantom and preclinical porcine DM-PCT dataset were used in the study. RESULTS The present PWLS-MASP algorithm can obtain high-quality DM-PCT images in both phantom and porcine cases, and outperforms the existing filtered back-projection algorithm and PWLS-based algorithms with total variation regularization (PWLS-TV) and robust principal component analysis regularization (PWLS-RPCA) in terms of noise reduction, streak artifacts mitigation, and time density curve estimation. Moreover, the PWLS-MASP algorithm can yield more accurate diagnostic hemodynamic parametric maps than the PWLS-TV and PWLS-RPCA algorithms. CONCLUSIONS The study indicates that there is a substantial advantage in using the present PWLS-MASP algorithm for low-dose DM-PCT, and potentially in other dynamic tomography areas.
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Affiliation(s)
- Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Zhang Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Changfei Gong
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Xiumei Tian
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Gang Yan
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Jing Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Hong Guo
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Bo Chen
- College of Mathematics and Statistics, Shenzhen University, Shenzhen, 518060, China
| | - Jing Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Wufan Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
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50
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Gu C, Zeng D, Lin J, Li S, He J, Zhang H, Bian Z, Niu S, Zhang Z, Huang J, Chen B, Zhao D, Chen W, Ma J. Promote quantitative ischemia imaging via myocardial perfusion CT iterative reconstruction with tensor total generalized variation regularization. ACTA ACUST UNITED AC 2018; 63:125009. [DOI: 10.1088/1361-6560/aac7bd] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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